23
Polyp-detection in Colonoscopy Stefan Ameling 2008 [email protected]

Polyp-detection in Colonoscopy

  • Upload
    lefty

  • View
    78

  • Download
    1

Embed Size (px)

DESCRIPTION

Polyp-detection in Colonoscopy. Stefan Ameling 2008 [email protected]. Medical Background: Colon. Length: ~ 1.5 m Diameter: ~ 6 cm „tubelike“. Medical Background: Colon. Common diseases: Colitis Diverticulitis Colon cancer. Colon cancer. 70.000 cases/year (in Germany) ‏ - PowerPoint PPT Presentation

Citation preview

Page 1: Polyp-detection in Colonoscopy

Polyp-detection in Colonoscopy

Stefan Ameling2008

[email protected]

Page 2: Polyp-detection in Colonoscopy

Medical Background: Colon

Length: ~ 1.5 m Diameter: ~ 6 cm „tubelike“

Page 3: Polyp-detection in Colonoscopy

Medical Background: Colon

Common diseases:

Colitis

Diverticulitis

Colon cancer

Page 4: Polyp-detection in Colonoscopy

Colon cancer

70.000 cases/year (in Germany)

one of the leading causes of cancer death worldwide: 655.000 deaths/year

Page 5: Polyp-detection in Colonoscopy

Colon polyps

Untreated polyps can develop into cancer.

Colonoscopy: cancer prevention (detect and remove polyps).

Problem: miss-rate (up to 25 %)

Page 6: Polyp-detection in Colonoscopy

System for computer-assisted detection

Supports the doctor during examination

Unsolved problem

Many approaches

Lack of good data

Page 7: Polyp-detection in Colonoscopy

General approach

Acquire data (videos / images) including ground truth information

Extract features

Train classifier

Test classifier

} find „the best“ features

Page 8: Polyp-detection in Colonoscopy

1. Data acquisition

Videos: Capture colonoscopy in hospital

„Ground truth“: time consuming

Page 9: Polyp-detection in Colonoscopy

2. Feature extraction

Divide image into patches

Extract features:TextureColor…

One feature-vector for each patch

Page 10: Polyp-detection in Colonoscopy

3. Classifier training

Example: Support Vector Machine (SVM)

We have: set of feature vectors, each belonging to one class (polyp or non-polyp)

SVM: Hyperplane

Page 11: Polyp-detection in Colonoscopy

4. Classifier testing

Test and training sets must be seperated! (e.g.: n-fold cross-validation)

Possible results for the patches:true positive (tp), false negative (fn)false positive (fp), true negative (tn)

Receiver Operation Characteristics (ROC) Graph

Ordinate: Abscissa:

Sensitivity= TPFNTP

Specificity= TNFPTN

Sensitivity 1−Specificity

Page 12: Polyp-detection in Colonoscopy

Our approach

We have: 4 hours video of colonoscopy Full HD (1920 x 1080)

4 scenes, each showing a different polyp Varying distance, angle, illumination

Texture feature extraction:Grey-level co-occurrence Matrix (GLCM)Local binary pattern

Page 13: Polyp-detection in Colonoscopy

Grey-level co-occurrence Matrix (GLCM)

GLCM Greyimage of size

Thus, is a matrix of size where is the number of possible grey-levels in

can be normalized by dividing each entry by

the sum of all entries (→ probabilties)

pi , j =∑n=1

N

∑m=1

M

{1 if I n ,m=i∧I n x ,m y= j0 else

pI N×M

p G×G GI

p

Page 14: Polyp-detection in Colonoscopy

GLCM: example

100

22

0 1 0

Image

2

200

02

1 1 0

GLCM (not normalized)

0

The GLCM is parameterized by and Here: and

d x d yd x=1 d y=0

Page 15: Polyp-detection in Colonoscopy

GLCM: statistical features

e.g. homogeneity:

Energy, correlation, inertia, …

These statistical features can form a feature-vector.

f homogeneity=∑i=0

G−1

∑j=0

G−1 p i , j 1i− j2

Page 16: Polyp-detection in Colonoscopy

Local Binary Pattern (LBP)

1286432

168

1 2 4

Weights

2367

1812

21 4 3

Example neighbourhood

9

010

11

1 0 01

+ 8+ 16+ 64

LBP = 89

LBP

LBP-value (computed from each neighbourhood)

All LBP-values form a histogram that can be used as a feature-vector

3×3

Page 17: Polyp-detection in Colonoscopy

Extension: Opponent-colour LBP

One LBP-histogram from each color-channel

Additionally: Intra-channel histograms:center-pixel and neighbourhood from different

color-channels

In total: 9 histograms form the feature-vector → many dimensions

Page 18: Polyp-detection in Colonoscopy

Experiments

Data: 4 scenes

Feature-extraction: 4 different featuresetsGLCM 6GLCM 16LBPOC-LBP

4 different patch-sizes

Classifier-training and testing (LibSVM) Stratified 4-fold cross-validation

Page 19: Polyp-detection in Colonoscopy

ROC-graph (example)

Page 20: Polyp-detection in Colonoscopy

Results (AUC)

Scene Patchsize GLCM6 GLCM16 LBPOC-LPB

1 70 0.83 0.95 0.89 0.94

2 70 0.70 0.75 0.76 0.87

3 70 0.89 0.88 0.95 0.96

4 70 0.65 0.68 0.80 0.91

1 50 0.80 0.89 0.86 0.89

2 50 0.71 0.75 0.71 0.84

3 50 0.80 0.83 0.88 0.95

4 50 0.63 0.65 0.77 0.89

1 35 0.77 0.92 0.84 -

2 35 0.65 0.71 0.66 -

3 35 0.74 0.76 0.82 -

4 35 0.63 0.65 0.74 -

Page 21: Polyp-detection in Colonoscopy

Results

OC-LPB almost always the best GLCM6 almost always the worst

GLCM performs worse on scene 4 LPB performs worse on scene 2

Independent from the features:Scene 1 and 3: good resultsScene 2 and 4: worse results

No relation between feature and „polyp- types“

Page 22: Polyp-detection in Colonoscopy

Future Work

More video/image data

Method for ground truth aqcuisition

Test / develop more features

Realtime

Page 23: Polyp-detection in Colonoscopy

References

GeneralAMELING, S.: Polypen- und Tumordetektion in Koloskopie-Videos, Studienarbeit im Studiengang

Computervisualistik, Universität Koblenz-Landau, 2008

Miss-ratesBRESSLER, Brian ; PASZAT, Lawrence F. ; VINDEN, Christopher ; LI, Cindy; HE, Jingsong ; RABENECK,

Linda: Colonoscopic miss rates for right sided colon cancer: a population-based analysis. In: Gastroenterology 127 (2004), Nr. 2, S. 452–456

THOMSON, Alan ; AHNEN, Dennis ; RIOPELLE, John: Intestinal polypoid adenomas. In: eMedicine, The Continually Updated Clinical Reference (2007)

Polyp Detection MethodsIAKOVIDIS, D.K. ; MAROULIS, D.E. ; KARKANIS, S. A.: An intelligent system for automatic detection of

gastrointestinal adenomas in video endoscopy. In: Computers in Biology and Medicine 36 (2006), Nr. 10, S. 1084–1103

Local Binary PatternsMäenpää, T.: The local binary pattern approach to texture analysis–extensions and applications. (2003)

Dissertation, University of Oulu.

Grey-level co-occurrence MatrixHARALICK, R. M. ; DINSTEIN, I. ; SHANMUGAM, K.: Textural features for image classification. In: IEEE

Trans. Systems, Man, and Cybernetics 3 (1973), Nr. 6, S. 610–621